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Mendeley readers
Attention Score in Context
| Title |
Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training
|
|---|---|
| Published in |
BMC Bioinformatics, March 2006
|
| DOI | 10.1186/1471-2105-7-125 |
| Pubmed ID | |
| Authors |
Michael Meissner, Michael Schmuker, Gisbert Schneider |
| Abstract |
Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Choosing reasonable parameter values for the PSO is crucial for its convergence behavior, and depends on the optimization task. We present a method for parameter meta-optimization based on PSO and its application to neural network training. The concept of the Optimized Particle Swarm Optimization (OPSO) is to optimize the free parameters of the PSO by having swarms within a swarm. We assessed the performance of the OPSO method on a set of five artificial fitness functions and compared it to the performance of two popular PSO implementations. |
Mendeley readers
The data shown below were compiled from readership statistics for 196 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 2% |
| Germany | 2 | 1% |
| Indonesia | 2 | 1% |
| Malaysia | 1 | <1% |
| Italy | 1 | <1% |
| Hong Kong | 1 | <1% |
| Australia | 1 | <1% |
| Portugal | 1 | <1% |
| United Kingdom | 1 | <1% |
| Other | 3 | 2% |
| Unknown | 180 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 43 | 22% |
| Student > Ph. D. Student | 40 | 20% |
| Student > Bachelor | 22 | 11% |
| Researcher | 21 | 11% |
| Student > Doctoral Student | 11 | 6% |
| Other | 36 | 18% |
| Unknown | 23 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 57 | 29% |
| Computer Science | 54 | 28% |
| Agricultural and Biological Sciences | 15 | 8% |
| Business, Management and Accounting | 6 | 3% |
| Physics and Astronomy | 6 | 3% |
| Other | 24 | 12% |
| Unknown | 34 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 February 2024.
All research outputs
#7,699,921
of 23,419,482 outputs
Outputs from BMC Bioinformatics
#3,062
of 7,383 outputs
Outputs of similar age
#24,460
of 70,278 outputs
Outputs of similar age from BMC Bioinformatics
#27
of 60 outputs
Altmetric has tracked 23,419,482 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,383 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 70,278 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 60 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.